International Business Machines Corporation (IBM) Earnings Call Transcript & Summary

Good morning. Please welcome to the stage Rob Thomas and Bruce Ross.

Well, good morning, everyone. It's nice to be here. As you wrap up your breakfast and enjoy the morning, so we're going to get started here right now with Rob Thomas. I'm delighted to have Rob Thomas, Senior Vice President, Chief Commercial Officer at IBM. I was going to say at RBC. No. I have -- I still have problems, Rob, because I've worked at IBM at one time, as you know, and I sometimes that slips in, but that was only a decade ago. So...

Right. Exactly.

It sticks with you forever.

We'll always welcome you back, Bruce, but you're doing great work at RBC. So...

Well, thanks. We could -- having an interview in front of 200 of your closest friends always a great thing. So anyway, thanks so much for joining us this morning. Just as we get started, Rob, you've had a wonderful career so far at IBM. And just maybe tell a little -- people a little bit by yourself and the journey that you've been on in your role today.

Yes. I started in consulting at IBM. So did that for a number of years and then moved to microelectronics because IBM used to be in semiconductor manufacturing, ASIC design. We still do a lot of chips through our own systems, but we spun off the microelectronics business a number of years ago. Lived in Japan for a couple of years. And then when I came back from Japan, I started in software. So I think that was 2006, 2007. And I've spent really all of my career in software since then. Did -- ran engineering for a while, spend a good bit of time on M&A, go to market. So really been focused on software for most of my career at this point.

Yes. Well, a few topics that I know everybody will be interested in getting into this morning. So thanks for that. And those of you who know that IBM, well, that's a meteoric career path and one that a lot of people like to duplicate. So congratulations on that.

The -- as we think about IBM, back 2019, pivotal year, Red Hat acquisition. I think it was 2021, the spin-off of Kyndryl as well. And so some big changes at the organization and strategically the repositioning of the company. I just wondered if you could give people some insight on to what your strategic direction is going forward and how this all plays into it for you?

I think Arvind laid it out really well to his credit when we took over in 2020. And at that time, he said we're going to be focused on hybrid cloud and AI. And I think at that moment, not everybody understood what that meant because while hybrid cloud that seems different than public cloud, we're moving everything to the public cloud. And I think the world has kind of come around to that view in the last few years that public cloud is critical, but most companies are going to do something on private cloud as well on the edge. And so we've put a lot of focus into hybrid cloud and what it means to be a great technology provider and consulting services provider on hybrid cloud. He also talked about AI at that point, and that was before the Netscape moment with ChatGPT. Interestingly, we actually started our investments in generative AI in 2020. It was probably one of the least popular investments at the time where we put a couple of hundred million dollars into building infrastructure to start doing model training. And this was before -- this was really on anybody's radar. I think probably the key reason for the progress we've made and there's still a lot more to do is it's really about focus. We said we're going to be hybrid cloud AI. That will be the focus of the company. When you choose those types of segments, working with partners becomes more important because you can't do hybrid on your own. And so we've put a lot of investments into ecosystem, we've put our software on to AWS and Azure. We've built consulting practices around AWS and Azure. And so there's never one thing, but I'd say just thoughtful focus in terms of strategic priorities that drives our product investment. It's also driven the investments that we've made in consulting. And I think we're getting some positive results, but there's always more to do.

Yes, I think you're also as a big consumer of your capability, I think you've also been the engine room for high-volume transaction businesses and one that we can really rely on. So it's -- those investments you talk about how they build on top of companies that process over 1 billion transactions a day has been pretty impactful.

I think to your credit, Bruce and your team, you guys push us constantly. And I think you're probably a perfect example of why we thought hybrid cloud would be a significant market, which is you guys were aggressive in moving applications to public cloud. At the same time, you leverage the mainframe. And so you're like, well, we need these things to talk to each other. We need a level of connectivity. I think you were one of our most significant early customers on OpenShift, Red Hat OpenShift for modernizing your applications. And I think it's been a really good partnership. And your team always pushes us. And even some of the stuff we started to do on AI, I think, are good examples. So I think it's a good example of technology driving business value, I would say, as I think about some of the things we're doing together around risk, payments, other areas, I think it's places that are important to RBC.

Yes, exactly. And especially in today's world, which stepping back for a second, just talking about the macro environment that we're in, we've got a lot of pressures going the wrong way on us in terms of climate change, interest rates going up, economies in trouble. And then obviously, global issues that are around the world in different places in different markets. You're in a unique position, I think, amongst firms, regardless of technology, but just firms in general being about 170-odd markets. What's the global economy look like going forward for you from a -- and how does that impact your investment thesis? Which parts of the world do you see are going to be better or worse? And how does the investments you're making going to be? How do you look at the juxtapose the two, tougher world and yet need to invest?

I do think it's very different based on where you go. And about, I guess, 2 years ago, we decided to get a little more aggressive in our investment in different markets around the world. And we were basically looking at what are $1 trillion GP countries that are growing north of 5%. So ideally, 8% to 10% range that are putting technology at the forefront of what the country's trying to do. And you look at that, it's not a huge list, but you see countries in the Middle East. UAE, Saudi Arabia would fall into that. India's in that category. Indonesia's in that category. And the challenges and opportunities there are very different because -- and some of those there's actually not a lot of inflation, as an example. And -- but what they all have in common is an aggressive growth agenda that's kind of driven tops down in the country and technology's at the forefront. And in many cases, they don't have all the skills they need. So that's a pretty good match for us. So I was in the Middle East, middle of October, and the progress we've made during the year has been remarkable to see. And we'll just keep going because they're going to keep going. The other interesting surprise to me -- probably the biggest surprises that doesn't get talked about much is what's happening in Japan. So people don't think of that as a high-growth market. But the government in Japan is probably more aggressive now than they've ever been on investment. We've announced the work that we're doing on semiconductor technology, which is really driven by the government sponsorship working with a company called Rapidus. And this has gone from an idea to ground has been broken. You can actually see the fab being built. And this is like in a 1-year period. I don't think I've ever seen Japan move this fast. And so I do think we're at a unique time where governments are realizing the importance of technology in terms of what they need to do as a country. And we want to be a part of that. I mean if you just think about GDP growth, I think classic equation is something like GDP growth is about population growth plus productivity growth plus debt. And not every country has the benefit of population growth. And so then it's going to be about productivity. That's really only going to come from technology. And then you know the credit part probably better than I do. So I'll leave that one to RBC.

Well, it's interesting. One of the things we think about quite a bit right now is the whole relationship with chips, and you talked about it. The United States position versus the Asian markets, the instability over there, the access to chips, your supply chains, global in nature, how do you think about that? The U.S. government has put a lot of money into chips, but that capacity, as you said, like building a fab plant takes years to bring online. Will the U.S. be self-sufficient in chips? Or is it actually, no, it's -- they're going to be better but not able to do it on their own?

Self-sufficient would be hard to imagine at this point. This takes a long time, right? If you look at what comes out of Taiwan today primarily. But there's also -- I mean, we have a big partnership with Samsung. So we get a lot of our chips out of Korea as well. But self-sufficient in the U.S. feels like a ways off. But I think the government is doing the right things to start thinking about investing. You talked before about sustainability, supply chains. Obviously, we spend a lot of time thinking about how do we de-risk what we're doing from a supply perspective, which I think we've done a good job on. The sustainability topic is one that comes up everywhere. About -- I think it's about 2 years ago, we actually announced a software product line focused on sustainability. And I think people are starting to realize now that sustainability is not just good for the climate but it's actually good for business because a lot of our work is around asset management, how do you optimize assets that are deployed, how do you extend useful life, predictive maintenance and analytics. And we're even doing ESG dashboards now. We bought a company called Envizi that kind of gives you a view of how are you doing against your goals to get to zero carbon emissions. And so I think sustainability has shifted from it's just the right thing to do to, it's also a good business, and that's been good to see.

Yes. It's a big thing for us, just as a big Canadian company, we become the poster child on climate. And it's a big deal, not just for us within the pressures that you see day-to-day, but it's actually what our clients are asking for. So the ESG strategy and specifically on climate is a huge deal to us. So thanks for that. Back to technology innovation. The -- would you juxtapose a bit of an uncertain marketplace. I know that spending is down certainly in the Western markets. And then you say, okay, but gee, there's all these great technologies that you see like artificial intelligence, quantum computing, Gen AI. I was at in Silicon Valley the other day and I -- about 3 months ago, and I'm starting to hear this term while there's classic AI, Bruce, and then there's Gen AI saying they're going really classic AI that's only been around for 2 years. We started investing in this in 2015 when we created Borealis AI, and we have about 100 PhDs that only focus on that today. You're a great partner of ours, and that -- and it's a big space, right? Breadth of space from everything from the compute power that you need to the data you organize to the LLMs you build and the capability you bring to market. Can you just talk to us for a second about you made some great investments and announcements, whether it was granted and more. Maybe just talk about -- so across the breadth of that space, where is IBM really putting its energy and where does it see it playing in this ecosystem?

I kind of laugh about your comment on old AI versus new AI. I forget who said this, but there's -- I think he's a computer scientist who made the comment that once it works, it's no longer called AI. So it's like a forever moving goalpost because, first, it was machine learning, data science, that's AI. Well, I know that works. So now it's no longer AI. So it will be a forever moving goalpost, I think, for everybody. But we're really excited about what we're doing with watsonx. We launched that in May. And watsonx is a platform for generative AI. So there's a studio, which is called watsonx.ai for building foundation models. And to your point, we've populated with some IBM models. We've also partnered with Meta to bring in Llama 2. We partnered and invested in Hugging Face to bring in open source model. So it's basically a way to build foundation models. We've got watsonx.data, which is a modern data architecture for AI. And just this week, we announced watsonx.governance, which probably has one of the -- a bigger response than I expected, which is really how do you monitor AI that's running in your company to understand how decisions are being made to be able to create a report that you can put in front of a regulator, which is very important regulated industry. So really happy with watsonx. We've also built a layer on top of watsonx that we call assistance. These are different more functional or line of business type use cases. We've got watsonx Orchestrate, which is for automating back-office tasks largely. I think one example in IBM was we've automated 90% of the work done in some of our HR service centers using Orchestrate, just a way to automate repetitive tasks. And probably one of the things I'm most excited about, which is related to your point on Granite is something called watsonx Code Assistant, which is about making developers more productive. Granite is a foundation model we built for code. We support about 115 different code languages. We started with COBOL because there's about 300 billion lines of COBOL in the world. Every customer is trying to figure out how do I run this more efficiently, how do I modernize applications because some may not want to run on the mainframe, some may want to keep on the mainframe. And so since we made that generally available a couple of weeks ago, I'd say the client interest has been off the charts. But interestingly, the biggest use case right now is not even necessarily modernization or migration. It's just code generation. If you don't have enough developers or you can't hire enough developers being able to use a Code Assistant just to generate more code can be hugely valuable. And with Code Assistant, we're seeing 85% acceptance rates, meaning watsonx will recommend the code, developers accepted 85% of the time, that's a huge productivity boost.

Yes. I think it's -- a couple of those areas that we're really taking advantage of inside the bank and I think actually others of our size would be absolutely doing is this whole area on -- if you think of DevOps is everything from that, it starts in agile with a team and it ends up in cloud. That code generation piece of it is the piece that hasn't been not highly automated. So Gen AI is an ability to drive that. We see it minimum of 30% productivity improvement in that code development space. So you start to translate that across thousands of developers. Now it will take time, but we see this as the next frontier of our efficiency and capability to get things to market. And I think that, that -- your COBOL piece like a lot of us, we -- while we talk about the new stuff, we talk about digital and everything else, we still sit on these massive legacy systems I talked about earlier. And the ability to be able to -- as our generation gets older and people start to retire, how do you ensure that you understand that and have that intellectual property on that code to be able to support it, manage it, transform it in the future? That's not easy. So I think this type of capability on the COBOL side, we had been making phone calls before where they got started on that to help with that space.

One thing that's interesting on this is that with Granite, it's -- the interesting thing about generative AI and foundation models is the model is generalized for code. So it supports 115-or-so languages. We fed it data for COBOL and for Red Hat Ansible. So those became the first two products because if you basically feed it a lot more code for those two, then you can actually turn into a product. But I think the ability to productize this is almost unlimited. We're thinking about doing code assistant for Java. We're working with a lot of banks. What I've learned is that a lot of banks have custom programming languages that they built somewhere in the '60, '70s or '80s on many trading floors. And now they're trying to figure out can we take code assistant and generalize to use for their languages, and we're finding actually very good results for that. So I think this area of code is just getting started.

Do you -- back to the LLMs and obviously compute has been an issue -- the amount of compute that you need has actually forced people into specific models. You see where open AI has been going. You've actually built a number of, what I call, purpose-driven models by functional capability. Sort of two questions like, one, do you see us continue to go down to that purpose-built LLMs? And then two, are they going to get smarter so we can run them with less compute underneath them?

I believe the only sustainable competitive advantage for generative AI is going to be proprietary data. So maybe not the models themselves. The models will be important. But if you can't feed it proprietary data that solves a business problem for your organization, then I don't think there will be a long-term ROI here. So why do I say that? I think the models itself are very important. And building a base foundation model, the investment is not for the faint of the heart. Round numbers, it feels like it's $500 million or so to build a base model. So I don't think you're going to see thousands of companies do that. But that's why we build base models. And then if we're working with somebody like RBC, you can bring your proprietary data to tune the model based on your data. And then effectively, that becomes your model. And I think that will become the default approach for many companies. We also announced about a month ago that we're now indemnifying our models, which to us was a key point on, if a client wants to work with us and tune based on their data and they're using our model, we think we have to stand behind it in terms of providing indemnification because it gives clients comfort to be aggressive in how they pursue this. So I don't think you're going to see every company building foundation models. I think there'll be a handful of those. There'll be a lot in open source. So we're doing a lot of work in PyTorch because we think that will be the driver of a lot of innovation around foundation models. But I think proprietary data, tuning models is going to be where a lot of the competitive advantage comes from.

Right. And then being able to -- for us, it's how do I create that proposition for the client where I can have a debate with you on your financial lifestyle on your mobile phone or whatever piece of glass you want, wherever you are, whenever you want to have that "debate". And I think that this is a key component of it. I think that, that -- now at the client side for financial institutions, the difficulty is how do you do it in a safe way that meets your own standards and the regulatory standards. And I think that, that indemnity that you talked about to us, that was really important. And -- because it's part of what we call an overall control point of ensuring that, yes, we've got bias management. We have explainability. We have a level of control that we know where the data is, who's accessing it, who's got the right to access it. And then that we can explain all that easily to a regulator and having the -- and we also get asked as a financial institution that anything that we take to market, we've got to be able to hold the third party responsible as well. And so that indemnity that you put forward is really part of what we would call a control point. So great. Thank you for that.

I think it's a key part. I think the other piece, and your team has been aggressive on this to their credit, is around governance. And what we've announced on watsonx.governance is really -- the analogy I would use is think about the -- when the FDA came out with the nutrition label, I think this is like the '60s, and it was people should know what they're putting in their bodies. They should know impact of that, that type of thing. This is basically a nutrition label for AI, which is do you understand what models are running, do you understand how they're performing, how does this correlate to the enterprise risk that you're willing to take, and I think that this is really going to take off. Now you probably don't need governance if you're running one model because you can put enough people around it to develop the reports, watch it. The minute you get to like tens, hundreds, thousands, hundreds of thousands, which is where most larger companies will get, you have to do this in an automated fashion. So you have to use AI to monitor the AI basically. And so I think we're just getting started there.

Let's talk about the data problem. You raised that, and we would absolutely agree with you. If you -- inside any institution, certainly a large one, we have these -- while we talk about external data and being able to use our external data, I don't think we leverage our internal data well enough. That's what everybody is looking for. You talked about organizing the data to be able to leverage the AI. So what's your perspective and your investment thesis around data, preparing the data so that you can take advantage of the models?

I'd like to say there's no AI without IA, meaning information architecture. And I think most companies still have a lot of catch-up to do on information architecture, data strategy to be ready to do AI at scale. I think we're at an interesting inflection point in data. If you look at data warehousing market, it goes back, I guess, 30, 40 years. And I think we're at like the fourth, I would call it, an epic of data warehousing. Like the first one was OLAP. Like people had databases. They started doing online analytical processing. And that led to the creation of Oracle, DB2. There was a bunch of players in that initial epic. The next one was really about appliances. We bought Netezza, it was a key part of IBM. Oracle did Exadata. Teradata started to get bigger. So appliances was kind of the second piece. I'd say the third wave is what we're -- I would say, we're a tail end of, which is the architecture around separated compute and storage. It was a huge unlock from appliances, but it's also very expensive. And you end up with your data kind of trapped in different silos. So you kind of recreated the problem somewhere else. The reason I say we're at the start of a new epic is there's a lot happening in open source right now, which is kind of what we've been contributing to and starting to commercialize. It's things like Iceberg, which is an open table format. So your data is no longer locked up. You can store it in an open format, so anything can access it. We're doing work with Presto, which was a query engine that came out of Facebook. And now the next version is called Velox, which is basically a high-performance query engine. And we made the product available in July. The results we're getting in proof of concepts are remarkable, like 2x performance over separated compute and storage architectures at like half the price. So I think there's going to be a major change in the data landscape. But this -- we've seen this before. I said there's 4 epics. It's like 4 years I think. It plays out over 10 years. But I think we're at the end of this last chapter. We're at the start of this next one. And I think AI will be the catalyst for this. Because the minute you get excited about AI, you just want more data. And you don't really want to have to deal with, I need to do a 90-day data quality project or a data movement project before I can get started. So I think open architecture is just going to win for data.

Yes. That whole point on do you move the data to the compute or the compute to the data and how that sits for us, it's federated. It is a hybrid cloud environment because we just can't move that kind of quantity of data. You think of like 1 billion events a day. You're not going to write them through your core systems and then write them into a public cloud, you just -- it'd just be...

You get killed on ingress and egress fees, right?

Yes. Yes. Yes. So how do you be able to operate across the two? Switching topics to on you if you're okay with it. It's cyber, right? So cyber -- ransomware activity is up over 70% this year. We have to -- we certainly have to look at not only for ourselves, but every single third party that we deal with, and we deal with over 1,000 of them. The world is getting more and more difficult in this space. You guys have made a lot of investments there. What's the future of cyber for IBM in terms of investments? And how do you position this? It's one of the areas where I go to Silicon Valley, they're over -- I think there are over 700 start-ups in the cyber space, and everybody has got a great point-to-point solution. But as an organization, for us, it's massively difficult to integrate these. So what's your point of view?

I think you just hit on one of the key challenges. The security market has always been incredibly fragmented. And you end up with a problem that you just described you all have, which is you've got thousands of tools and you become the integrator, which is not really ideal from a client perspective. So as we think about our security software, one of our big focuses is integrating us as a platform. We have a great installed base with QRadar. We're kind of at the heart of a lot of companies in terms of how they administer, monitor and think about their cyber security strategy. We have more to do to bring that together as a platform with all the -- you want to integrate identity, you want to integrate threat management. So more work to do there. The area I'm probably most excited about is data security. I think if you look at cyber as a software market, that's probably been the place that's been ignored for the last decade. There was a lot of focus on data security around 2010. We acquired a company by the name of Guardium, which has done incredibly well. There's like a couple of other players in the market, but there hasn't really been a next wave of data security. That's what we started on now. It's an organic build that we've been doing. We also acquired a company called Polar Security, which has a great team and some great assets to augment what we're doing. I think this notion of multi-cloud or hybrid cloud data security is going to be at the forefront for the reasons on ransomware that you described. So that's probably one area I'm very interested in that we're investing in.

Yes. And then back to the -- that -- not that I want to talk about legal terms all day, but that indemnity that you put into place, the work that you're doing on your own control plane, how that all comes together in front of our own risk committee of our Board, our regulator, ourselves as we manage it. It's all massively important. In fact, talking to some smaller regional banks, we've talked to in the past, a lot of -- some of them because they haven't been able to make that kind of investment in the security side or the regulatory side has actually held them back from being able to move things into the cloud environment. So you will actually slow yourself down if you can't make those investments. It is our -- cyber is our highest growth area of spend on a percentage basis not on a pure dollar basis.

So if you think about it in the last -- say, the last 20 years, I mean, how many technology topics have really become Board-level topics? Certainly, cybersecurity has. I think generative AI has. That's all happened in the last 9 to 12 months. It's not often that what you and I do every day become something that become as heavily discussed at every board room. But I feel like those are the two topics where it's certainly happening. Do you agree with that?

Yes. Board meetings are nice to avoid. You have to take a lot of preparation. And you're right, like if you ask Dave McKay, what are the top three risks that he deals with, I can absolutely tell you that cyber would be #1 or #2 that he worries about. I said, well, boos, like that's my job to worry for you. But -- and even with me worrying for him, he still worries about that in one of the top three. So you're right. And I think that the regulatory standard keeps going up, and it needs to in many respects. And the United States as a market leads that globally. So you'll see that. So you hit the regulatory standard here and then you tend to be able to achieve it around the world. So we only have a few minutes left. I just wanted to come back to M&A. You've talked a lot of our M&A activity. I know everybody here is interested in, hey, what's IBM's next move? You made a huge acquisition yourself with Apptio in the FinOps space. So you've got a lot of things that are on your mind. How do you look at the market space for IBM's direction with organic growth with $6 billion plus of R&D spend, but obviously, a market there where you can -- you make acquisitions. How do you think about this, Rob, and just generally your areas of interest?

What was balancing this? I mean, focus for me every day when I come into the office is really on organic innovation. How are we driving R&D. That's why we're really pleased with things like watsonx because I think we're getting our rhythm back in IBM in terms of being a great innovator, building new products. But M&A is a great way to accelerate things because on one hand, we could probably build anything, but that's probably not the fastest way to get to market because sometimes somebody else has a couple of year head start and we can capitalize on that, we can get it to a lot more clients way faster and drive growth through M&A. So I think Apptio is a good example. We probably could have done that. It probably would have taken us 10 years because they've done an incredible job. They've collected data on $450 billion worth of IT spend. You don't get that overnight. And so acquiring that, put us in a unique position where we can really be an adviser to clients on your technology spend how can you better optimize it. The part of Apptio that most people haven't focused on, a little bit lesser known is something called Targetprocess. And that is about the labor that goes against the technology spend. So everybody focuses on cloud spend, technology spend, makes sense. It's kind of easy. I mean, with Apptio, you can figure it out like a couple of weeks, how are you going to save money. But what the labor piece brings is what is my total cost going against any given project. And I'll tell you, when we sit in front of a CIO and say, here's the technology spend, the cloud spend, and then, by the way, the labor against that, there's like an aha moment. And so I think we're just getting started with Apptio. We feel really good about that. And we're always looking for what else we should be doing in M&A. I think we've done 35-or-so acquisitions since Arvind took over, about 70%, 80% in software. The rest in consulting. So it will remain a key part of our capital allocation strategy and growth strategy.

Yes. We were an Apptio client before IBM purchased them. And for us, we publicly said, we continue to focus on our efficiency, and this is actually one of the core components of us being able to really not only understand that spend but manage the spend by business units. So from my seat, I can have a very fact-based discussion with any of our business leaders on exactly what those investments are. I think banks are really good at managing their costs and less good at actually managing the benefit of those investments and realizing those benefits over time. This is the type of thing that really helps with that. I'd like to just end on leadership. So Rob, you may or may not know this, but Rob's got a blog that is publicly accessible. I spent some time on that. And one of the things you talked about most recently was excellence. And I just thought like when I think about what you're doing with IBM, your focus on excellence, Rob, and how the IBM company is turning itself around, I know you're mid-flight on that, but you made really substantial progress in the last number of years, maybe just talk a little bit about you're managing such a huge globally dispersed team, great communication method of it. But just your thoughts on excellence and what that means to you and your team.

I think when you look at businesses that are struggling, they normally have I'd say two problems. They're probably unclear on priorities and cost is probably in the wrong places. And so I think primary role of a leader is to figure out those two things. How do we define priorities and get everybody aligned to those priorities. And then how do you allocate capital. So that cost is in the right places as opposed to the wrong places. And to your point on large organization, distributed teams, you can't just say, these are the priorities and assume it will get done. So the question is, how are you going to motivate people? How are you going to excite them? So I do spend a lot of time thinking about that. I actually write more stuff behind the firewall internally than I do externally because I found it's a good way for me to kind of get my own thoughts together. It's like if you have to sit down and say what do you want to get done in the next year, it's actually really hard to put that on a sheet of paper. And like the first five versions are normally really bad. But you start to refine it, you get it to a level of simplicity you think people can understand it, then it becomes a really good way to communicate what we're doing. And so I share a lot of writing internally because, one, it's a way to convey this is what we want to do, this is what's important, but you also get a lot of feedback on, well, this is why that's a bad idea. The thing that's great about IBMers is they all speak their mind. And so you can quickly learn what's going to work and what's not going to work. So I think writing is an underrated leadership tool for just for communicating. It's like there's something about people being able to sit down and like really see what you're thinking. You can do videos and stuff like that, and we do that. But I think there's something unique about writing. So that's why I like doing that.

Yes. Well, it's a -- I think really good insight there. And just as big organizations, I think our -- the need for us to be able to compress those organizational structures, I mean, in the days when we started working, you knew there was a CEO or a senior executive team and they're way up in the stratosphere, you never heard from them other than maybe once a year you got a note. And now there's that ongoing dialogue some of which is great, some of which you want to hide under your desk. But I think really great. It was great to have you here. Thank you very much for your openness on what IBM is up to and the things you're doing. And thank you -- as a client of yours, I just wanted to say a big thank you for all that you and your colleagues do for us. So please join me in thanking Rob for being here this morning.

Thank you, Bruce.